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Abstract
Machine learning (ML)-based prediction of non-linear composition-strength relationship in concretes requires a large, complete, and consistent dataset. However, the availability of such datasets is limited as the datasets often suffer from incompleteness because of missing data corresponding to different input features, which makes the development of robust ML-based predictive models challenging. Besides, as the degree of complexity in these ML models increases, the interpretation of the results becomes challenging. These interpretations of results are critical towards the development of efficient materials design strategies for enhanced materials performance. To address these challenges, this paper implements different data imputation approaches for enhanced dataset completeness. The imputed dataset is leveraged to predict the compressive and tensile strength of concrete using various hyperparameter-optimized ML approaches. Among all the approaches, Extreme Gradient Boosted Decision Trees (XGBoost) showed the highest prediction efficacy when the dataset is imputed using k-nearest neighbors (kNN) with a 10-neighbor configuration. To interpret the predicted results, SHapley Additive exPlanations (SHAP) is employed. Overall, by implementing efficient combinations of data imputation approach, machine learning, and data interpretation, this paper develops an efficient approach to evaluate the composition-strength relationship in concrete. This work, in turn, can be used as a starting point toward the design and development of various performance-enhanced and sustainable concretes.
Highlights
Concrete is considered the most widely used construction material in the world
This paper presents the strength prediction for concretes using various Machine learning (ML) approaches, including polynomial regression (PR), least absolute shrinkage and selection operator (LASSO), support vector machine (SVM), random forest (RF), XGBoost, and neural network (NN)
Among all the trained models, XGBoost and NN exhibit excellent prediction efficacy when k-nearest neighbors (kNN) with ten neighbors (k=10) was leveraged for imputation of the missing data
Summary
Concrete is considered the most widely used construction material in the world. The mechanical performance of concrete is primarily characterized in the industry by its 28-days compressive strength. With the growing focus on fundamental modifications in concrete towards reducing the environmental footprint, the evaluation of flexural and tensile strengths [1] has become increasingly important. Evaluating strengths has become more critical considering the emergence of concretes with supplementary cementitious materials or alternative binders for multifunctional applications and improved sustainability credentials. Accurate prediction of the concrete strengths significantly impacts the efficiency of the material usage and structural safety in civil infrastructure [2]. Underestimating concrete strengths can lead to excess cement usage, associated with a significant increase in CO2 emissions [3]
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